Exemptions in Cook County - Tax Year 2023

library(tidyverse)
#library(ptaxsim)
#library(DBI)
library(httr)
library(jsonlite)
library(glue)
library(sf)
library(DT)
library(flextable)


knitr::opts_chunk$set(warning = FALSE, message = FALSE)

options(scipen = 999)

muni_shp <- read_sf("https://gis.cookcountyil.gov/traditional/rest/services/politicalBoundary/MapServer/2/query?outFields=*&where=1%3D1&f=geojson")


nicknames <- readxl::read_excel("../Necessary_Files/muni_shortnames.xlsx")

Cook County’s Use of Homestead Exemptions

Figure 1 or 2?. Exemption Use

muni_sums <- read_csv("../Output/ptaxsim_muni_level_2006to2023.csv") |> 
 left_join(nicknames)


muni_mc_sums <- read_csv("../Output/ptaxsim_muni_MC_2006to2023.csv") |>
  rename_all(~str_replace(., "muni_mc_", ""))


muni_cl_sums <- read_csv("../Output/ptaxsim_muni_class_summaries_2006to2023.csv") |>
  left_join(nicknames)

year_examples <- c(2023)

for(i in year_examples){
  tbl <- muni_cl_sums %>% 
  filter(year == i) %>%
  group_by(year) %>% 
  summarize(AV = sum(muni_c_av), 
            EAV = sum(muni_c_eav), 
            Eq_AV = sum(muni_c_equalized_av),
            'Taxed EAV' = sum(muni_c_current_taxable_eav),
            'All Exemptions' = sum(muni_c_all_exemptions), 
            'GHE' = sum(muni_c_exe_homeowner), 
            'Senior Exemp.' = sum(muni_c_exe_senior), 
            'Freeze Exemp.' = sum(muni_c_exe_freeze), 
            'PINs in Muni' = sum(muni_c_pins_in_muni),
            'PINs with Exemptions' = sum(muni_c_has_HO_exemp))  %>% 
  pivot_longer(cols = c(AV:'PINs with Exemptions'), 
               names_to = "Totals", values_to = "Values") 
print(tbl)
}

# A tibble: 10 × 3
    year Totals                      Values
   <dbl> <chr>                        <dbl>
 1  2023 AV                    78873223159 
 2  2023 EAV                  237900442609 
 3  2023 Eq_AV                237905309979 
 4  2023 Taxed EAV            198837987383.
 5  2023 All Exemptions        15576109524 
 6  2023 GHE                    9888668957 
 7  2023 Senior Exemp.          2714623787 
 8  2023 Freeze Exemp.          2884707583 
 9  2023 PINs in Muni              1864161 
10  2023 PINs with Exemptions      1000590 

Figure 3. Percent Exempt

Total value of exemptions as a share of residential EAV, 2023

year_examples <- c(2023)

for(i in year_examples){

median_exempt <- muni_sums %>%
  filter(year==i)%>%
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv
) %>% 
  select(pct_fmv_exempt)

midpoint = scales::percent(median(median_exempt$pct_fmv_exempt), accuracy = 0.01)

print(muni_sums %>%
  filter(year==i)%>%
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv ) %>%
    mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) %>%
 full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
  ggplot(aes(fill = pct_fmv_exempt)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void()+ 
  labs(title = paste0(i), 
       subtitle =  "Exempt FMV / Municipality FMV",
       caption = sprintf("The municipality median is %s", midpoint)) +
    theme_void() + 
  theme(axis.ticks = element_blank(), 
        axis.text = element_blank())+
   scale_fill_steps2(high = "darkblue", low = "black",  mid = "beige",
                       n.breaks = 7, show.limits=TRUE,
                       na.value = NA,
                    nice.breaks = FALSE,
                   midpoint = median(median_exempt$pct_fmv_exempt),
                        name = "% FMV \nthat is exempt", label = scales::percent))
}

year_examples <- c(2023)

for(i in year_examples){

median_exempt <- muni_sums %>%
  filter(year==i)%>%
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv
) %>% 
  select(pct_fmv_exempt)

midpoint = scales::percent(median(median_exempt$pct_fmv_exempt), accuracy = 0.01)

print(muni_sums %>%
  filter(year==i)%>%
  mutate(pct_fmv_exempt = muni_fmv_exempt / muni_fmv ) %>%
    mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) %>%
 full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%
  ggplot(aes(fill = pct_fmv_exempt)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void()+ 
  labs(title = paste0(i), 
       subtitle =  "Exempt FMV / Municipality FMV",
       caption = sprintf("The municipality median is %s", midpoint)) +
    theme_void() + 
  theme(axis.ticks = element_blank(), 
        axis.text = element_blank())+
   scale_fill_steps(high = "darkblue", low = "pink",
                       n.breaks = 5, show.limits=TRUE,
                    limits = c(0, 0.35),
                       na.value = "gray50",
                    nice.breaks = FALSE,
                        name = "% FMV \nthat is exempt", label = scales::percent))
}

Figure 4. Exempt Tax Base in Cook County

muni_cl_sums %>% 
  filter(year == 2023) %>% 
  select(clean_name, muni_c_exe_homeowner:muni_c_exe_vet_dis) %>%
  mutate(clean_name = ifelse(is.na(clean_name), "Unincorporated", clean_name),
    Geography = ifelse(clean_name == "Chicago", "City", "Suburbs"),
         Geography = ifelse(is.na(clean_name), "Unincorporated", Geography),
         Geography = factor(Geography, levels = c("Suburbs", "City", "Unincorporated"))) %>%
  group_by(Geography) %>%
  summarize("GHE" = sum(muni_c_exe_homeowner, na.rm=TRUE),
            "Senior Homestead" = sum(muni_c_exe_senior, na.rm=TRUE),
            "Senior Freeze" = sum(muni_c_exe_freeze, na.rm=TRUE),
            "Other Exemptions" = sum(muni_c_exe_longtime_homeowner+muni_c_exe_disabled+muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70)) %>%
  pivot_longer(cols = c(`GHE`:`Other Exemptions`), names_to = "Type" ) %>%
   mutate(Type = factor(Type, levels = c("GHE", "Senior Homestead", "Senior Freeze", "Other Exemptions" 
                                         #"Senior Freeze", "Senior Homestead", "GHE"
                                        )
                                        )) %>%
  ggplot(aes(x=Type, y = value/1000000000, fill = Geography
             )) +
  
  geom_col(
           position = "dodge" 
          ) + 
  theme_minimal() +
  labs(title = "Exempt EAV in Cook County, Tax Year 2023", 
       subtitle = "by Type and Geography", 
       y = "EAV (Billions)", x = "Exemption Type") + 
  scale_y_continuous() +
  scale_fill_manual(values = c("blue3", "deepskyblue3", "gray"), )

Table 4.1: Total Exempt EAV in 2023 in the City of Chicago and the Suburbs

Exempt Tax Base in Cook County by exemption type and geography, for tax year 2021

Figure 5. Value of residential exemptions by type in Cook County, 2006 - 2023

muni_cl_sums %>% 
  select(year, muni_c_exe_homeowner:muni_c_exe_vet_dis) %>%
  group_by(year) %>%
  summarize("GHE" = sum(muni_c_exe_homeowner, na.rm=TRUE),
            "Senior Homestead" = sum(muni_c_exe_senior, na.rm=TRUE),
            "Senior Freeze" = sum(muni_c_exe_freeze, na.rm=TRUE),
            "Other Exemptions" = sum(muni_c_exe_longtime_homeowner + muni_c_exe_disabled + muni_c_exe_vet_returning+ muni_c_exe_vet_dis_lt50 + muni_c_exe_vet_dis_50_69+muni_c_exe_vet_dis_ge70, na.rm = T)) %>%
  pivot_longer(cols = c(`GHE`:`Other Exemptions`), names_to = "Type" ) %>%
  mutate(Type = factor(Type, levels = c(#"GHE", "Senior Homestead", "Senior Freeze", 
                                        "Other Exemptions", "Senior Freeze", "Senior Homestead", "GHE"))) %>%
  ggplot(aes(x=year, y = value/1000000000, fill = Type)) +
  
  geom_bar( stat = "identity" ) + 
  theme_minimal() +
  labs(title = "Residential Exempt EAV in Cook County", 
       subtitle = "by Type and Geography, 2006-2023", 
       y = "EAV (Billions)", x ="") +
  scale_fill_manual(values = c("chartreuse2",  "darksalmon", "brown2", "blue4" )) +
                    scale_x_continuous(breaks = c(2006, 2010, 2015, 2020, 2023))

Effect on Composite Tax Rates

cross_county_lines <- c("030440000", "030585000", "030890000", "030320000", "031280000",
                        "030080000", "030560000", "031120000", "030280000", "030340000",
                        "030150000","030050000", "030180000","030500000", "031210000")

eq_factor <- 3.02


incentive_majorclasses <- c("6", "7A", "7B", "8A", "8B")

commercial_classes <- c(401:435, 490, 491, 492, 496:499,
                        500:535,590, 591, 592, 597:599, 
                        700:799,
                        800:835, 891, 892, 897, 899)  

industrial_classes <- c(480:489,493, 
                        550:589, 593,
                        600:699,
                        850:890, 893
                        )

ptax_pins <- read_csv("../Output/Dont_Upload/0_Joined_PIN_data_2023.csv") %>% select(-c(eq_av, propclass_1dig))


ptax_pins <- ptax_pins %>% 
  mutate(class_1dig = str_sub(class, 1,1),
         class_group = case_when(
          (class_1dig == 5 & class %in% commercial_classes) ~ "5A",
          (class_1dig == 5 & class %in% industrial_classes) ~ "5B",
          class_1dig == 7 &  class < 742 ~ "7A",
          class_1dig == 7 &  class >= 742 ~ "7B",
          (class_1dig == 8 & class %in% commercial_classes ) ~ "8A",
          (class_1dig == 8 & class %in% industrial_classes ) ~ "8B",
          TRUE ~ as.character(class_1dig))) %>%
  mutate(
    # taxing district revenue = taxable eav * tax rate so rearrange the formula:
    taxed_eav = final_tax_to_dist / tax_code_rate*100,
    
    total_value_eav = (final_tax_to_dist + final_tax_to_tif)/ tax_code_rate * 100 + all_exemptions + abatements,
    
    exempt_eav_inTIF = ifelse(in_tif == 1, all_exemptions, 0),
    
    exempt_eav = all_exemptions + abatements,
    
    taxed_av =  taxed_eav / eq_factor, # current value that taxing agencies can tax for their levies
    
    ## taxable AV = equalized assessed value net TIF increments, gross exemptions. 
    ## Used for calculating untaxable value further below
   # taxable_av = (final_tax_to_dist / tax_code_rate *100 + all_exemptions + abatements)/ eq_factor, 
  
    # taxable_eav_fromincents =  ifelse(class >=600 & class < 900, taxable_av * eq_factor, 0),

      ## untaxable value = exempt EAV from abatements and exemptions + TIF increment
    untaxable_value_eav = all_exemptions + abatements + 
      ## TIF increment EAV above frozen EAV, which becomes TIF revenue
      (final_tax_to_tif /  tax_code_rate*100) +
      
      ## difference between 25% and reduced level of assessment for incentive class properties. Excludes TIF increment when calculating the difference! 
      ifelse(between(class, 600, 899), 
             (taxed_av/loa*0.25 - taxed_av)*eq_factor, 0),
   
    untaxable_incent_eav = ifelse(between(class, 600, 899), 
             (taxed_av/loa*0.25 - taxed_av)*eq_factor, 0),
   
    #  manually adjust untaxable value of class 239 properties
    untaxable_value_eav = ifelse(class == 239, 
                                 equalized_av-taxed_eav, untaxable_value_eav), 
    
    untaxable_value_av = untaxable_value_eav / eq_factor,
    untaxable_value_fmv = untaxable_value_av / loa,

    exempt_fmv = exempt_eav / eq_factor / loa, 
    
    fmv_inTIF = ifelse(in_tif==1, av/loa, 0),
   
    fmv_tif_increment = ifelse(final_tax_to_tif > 0, 
                               ((final_tax_to_tif / (tax_code_rate/100)) / eq_factor ) / loa, 0),
    fmv_incents_inTIF = ifelse(between(class, 600, 899) & in_tif == 1, 
                               fmv, 0),
    fmv_incents_tif_increment = ifelse(between(class, 600, 899) & final_tax_to_tif > 0 , 
                               ((final_tax_to_tif / (tax_code_rate/100)) / eq_factor ) / loa, 0),
    naive_rev_forgone =  untaxable_incent_eav * tax_code_rate/100) %>%
  select(tax_code, class, pin, fmv,
         untaxable_value_fmv, fmv_inTIF, fmv_tif_increment, fmv, total_billed, final_tax_to_dist, final_tax_to_tif, tax_code_rate, eav, equalized_av, av, everything())

muni_ratechange <- ptax_pins %>%
  mutate(class = as.numeric(class)) %>%         # Allows for joining later
  select(-c(propclass_1dig:av.y)) %>%
  filter(!clean_name  %in% c("Frankfort", "Homer Glen",  "Oak Brook", "East Dundee", "University Park",  "Bensenville", "Hinsdale", "Roselle", "Deer Park", "Deerfield")) %>% 
  # filter(!agency_num %in% cross_county_lines) %>%
  group_by(clean_name) %>%
  summarize(
    classgroup_PC = n(),
    # projects = n_distinct(both_ids), # mostly for industrial and commercial properties
    pins_withincents = sum(ifelse(class >= 600 & class < 900, 1,0)),
    fmv_incentive = sum(ifelse(class >=600 & class <900, fmv, 0), na.rm = TRUE),
    #fmv_taxed =  sum(taxed_fmv, na.rm=TRUE),
    fmv_incents_inTIFs = sum(ifelse(class >=600 & class <900 & final_tax_to_tif > 0, fmv, 0), na.rm = TRUE),
    fmv_inTIF = sum(ifelse(final_tax_to_tif > 0, fmv, 0), na.rm=TRUE),
    fmv_tif_increment = sum(fmv_tif_increment, na.rm=TRUE),
    fmv_untaxable_value = sum(untaxable_value_fmv , na.rm=TRUE),
    fmv_exemptions = sum(all_exemptions/eq_factor/loa, na.rm=TRUE),
    fmv_abatements = sum(exe_abate/eq_factor/loa, na.rm=TRUE),
    zero_bill = sum(zero_bill, na.rm=TRUE),
    fmv_residential = sum(ifelse(class %in% c(200:399), fmv, 0), na.rm = TRUE),
    fmv_C2 = sum(ifelse(class %in% c(200:299), fmv, 0), na.rm = TRUE),
    
    fmv_industrial = sum(ifelse(class %in% industrial_classes, fmv, 0), na.rm = TRUE),
    fmv_commercial = sum(ifelse(class %in% commercial_classes, fmv, 0), na.rm = TRUE),
    
    current_rate_avg = mean(tax_code_rate),
    avg_C2_bill_noexe = mean(ifelse(between(class,200,299) & all_exemptions == 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    avg_C2_bill_withexe = mean(ifelse(between(class,200,299) & all_exemptions > 0, (final_tax_to_dist + final_tax_to_tif), NA), na.rm=TRUE),
    av_taxed = sum(taxed_av, na.rm = TRUE),
    untaxable_value_av = sum(untaxable_value_av, na.rm=TRUE),
    av = sum(av),
    eav_taxed = sum(taxed_av*eq_factor), 
    eav_untaxable = sum(untaxable_value_eav, na.rm=TRUE),
    eav_tif_increment = sum(final_tax_to_tif/tax_code_rate, na.rm=TRUE),
    eav_max = sum(fmv*loa*eq_factor, na.rm=TRUE),
    fmv = sum(fmv, na.rm=TRUE),
    pins_in_class = n(),
    all_exemptions = sum(all_exemptions),   # in EAV
    abatements = sum(exe_abate),            # in EAV
    eav_incents_inTIFs = sum(ifelse(class >=600 & class <=900 & in_tif == 1, eav, 0), na.rm = TRUE),
    final_tax_to_dist = sum(final_tax_to_dist),
    final_tax_to_tif = sum(final_tax_to_tif),
    eav = sum(eav),
    new_TEAV_noIncents = sum(ifelse(class >=600 & class <900,
                                    (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor), na.rm=TRUE),
    
    ####### Not used currently
    # new_TEAV_noC6 = sum(ifelse( class >=600 & class <700, 
    #                             (taxed_av*eq_factor/loa)*0.25 , taxed_av*eq_factor)),
    # new_TEAV_noC7 = sum(ifelse(class >=700 & class <800,
    #                            (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor)),
    # new_TEAV_noC8 = sum(ifelse(class >=800 & class <900, (taxed_av*eq_factor/loa)*0.25, taxed_av*eq_factor)),
    # 
    #######

     new_TEAV_vacant_noIncents = sum(ifelse(class >=600 & class <900,
                                            0, taxed_av*eq_factor))
    ) %>%
  mutate(
    
    new_TEAV_noExemps = eav_taxed + all_exemptions, # does not include abatements
    new_TEAV_noAbates = eav_taxed + abatements, # include only abatements, not other exemption types
    
    # amount of EAV from taxing an additional 15% of the AV if incentive properties didn't exist
    forgone_EAV_incent = #class_group %in% incentive_majorclasses,
      #incent_prop == "Incentive", 
      new_TEAV_noIncents - eav_taxed) %>%
  #cbind(table_cook) %>%
  mutate(
    # Absolute maximum TEAV: No Exemptions, no abatements, no TIFS, no Incentive properties
    # Commercial and industrial assessed at 25%
    TEAV_max = eav_taxed + all_exemptions + abatements + eav_tif_increment + forgone_EAV_incent,
    
    # no exemptions or incentive classifications:
    TEAV_neither = eav_taxed + all_exemptions + forgone_EAV_incent,
    
    rate_noExe = final_tax_to_dist / new_TEAV_noExemps * 100,
    rate_noAbate = final_tax_to_dist / new_TEAV_noAbates * 100,
    rate_noInc = final_tax_to_dist / new_TEAV_noIncents * 100,
    rate_neither = final_tax_to_dist / TEAV_neither * 100, 
    rate_noTIFs = final_tax_to_dist / (eav_taxed + eav_tif_increment) * 100,
    rate_vacant = final_tax_to_dist / new_TEAV_vacant_noIncents* 100,
    rate_lowest = final_tax_to_dist / TEAV_max * 100,
    # rate_noC6 = levy / new_TEAV_noC6 * 100,
    # rate_noC7 = levy / TEAV_noC7 * 100,
    # rate_noC8 = levy / TEAV_noC8 * 100,
    rate_current = final_tax_to_dist / eav_taxed * 100,
    change_noInc = rate_current - rate_noInc,
    change_neither = rate_current - rate_neither,
    change_noTIF = rate_current - rate_noTIFs,
    change_noExe = rate_current - rate_noExe,
    change_vacant = rate_current - rate_vacant,
    change_lowest = rate_current - rate_lowest
  )  %>%
     mutate(across(contains("rate_"), round, digits = 2)) %>%
     mutate(across(contains("change_"), round, digits = 2))

write_csv(muni_ratechange, "../Output/muni_ratechange_2023.csv")

muni_ratechange <- read_csv("../Output/muni_ratechange_2023.csv")

muni_ratechange %>% 
  select(clean_name, starts_with("change_")) %>% 
  DT::datatable(rownames = FALSE)

Figure 6. Composite property tax rates with and without homestead exemptions, tax year 2021

Figure 6 will not be recreated but is just a bar chart of the change in tax rate in the table below

Table 1. Change in composite property tax rates due to exemptions, tax year 2023.

muni_ratechange %>% 
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe, final_tax_to_dist 
         ) %>% 
  DT::datatable(rownames = FALSE, colnames = c('Municipality' = 'clean_name','Current Comp. Rate' = 'current_rate_avg', 'Hypothetical Rate' = 'rate_noExe',  'Composite Tax Rate Change'='change_noExe', 

                       'Composite Levy' = 'final_tax_to_dist'),
          caption = "Table 1: Current and Hypothetical Composite Tax Rates if GHE $0") %>%
  formatCurrency('Composite Levy', digits = 0)

Table 4.2: Searchable table containing all municipalities and the hypothetical tax rate change if exempt EAV became taxable.

muni_ratechange %>%
  select(clean_name,  current_rate_avg, rate_noExe, change_noExe 
) %>%
  arrange(desc(change_noExe)) %>%
  slice(c(1:5, 69:73, 121:125)) %>%
    #slice(c(1:5, 58:62, 115:119)) %>%

  flextable() %>% 
  border_remove() %>%
  hline_top() %>%
  hline(i = c(5,10)) %>%

  set_header_labels(
    clean_name = "Municipality", 
    current_rate_avg = "With Exemptions",
    rate_noExe = "Without Exemptions",
    change_noExe = "Percent Point Difference"
    
    ) %>%
  align(j = 2, align = "right") %>%
align(j=2, align = "right", part = "header") %>%
set_table_properties( layout = "autofit") %>% bold(i = 8) 

Municipality	With Exemptions	Without Exemptions	Percent Point Difference
Phoenix	27.48	13.81	13.59
Park Forest	23.61	18.33	5.22
Riverdale	25.38	20.22	5.01
Dolton	20.11	15.62	4.31
Calumet City	20.69	16.94	4.04
Riverside	10.60	9.65	0.95
Orland Hills	9.04	7.95	0.93
Palatine	9.83	8.90	0.92
Broadview	10.43	9.51	0.90
Merrionette Park	9.75	8.85	0.90
Winnetka	7.69	7.51	0.18
Rosemont	8.97	8.76	0.08
Hodgkins	9.14	8.74	0.07
Mc Cook	10.80	10.42	0.06
Bedford Park	11.30	11.25	0.05

Table 4.3: Includes all exemption types in calculation of rate change.

Figure 7. Map of Spatial Patterns in Composite Tax Rate Change

Change in composite tax rate if exempt EAV was added back to the the taxable tax base (i.e. if there were no exemptions).

muni_ratechange  %>%
  left_join(nicknames, by = "clean_name") %>%
     #mutate(burden_current = ifelse(burden_current>1, 1, burden_current)) %>%

    # filter(major_class_code == 2) %>%
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name),
         shpfile_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) %>%
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%

  ggplot(aes(fill = change_noExe)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
   scale_fill_stepsn(colors = c( "#F7FEF5",
                                # "#e4f1e0",
                                "#d4f6cc",
                                "#47ba24",
                                "#1F6805",
                                "#133C04"),
                        show.limits=TRUE, 
                     limits = c(0, 15),
                     breaks = c(0, 2.5, 5, 7.5, 10, 15),
                     na.value = NA,
                        name = "Rate Change from \nExemptions"  
                    )

Effect on Tax Burdens

Figure 8. Dolton example of Share of levy paid by property type

CMAP used a donut chart, we will use a bar chart that represents 100% on the x a axis.

taxcodes_current <- ptax_pins %>% 
  group_by(tax_code) %>%
  summarize(
    av = sum(av),
    eav = sum(eav),
    equalized_AV = sum(equalized_av),
    pins_in_class = n(),
    current_exemptions = sum(all_exemptions),
    HO_exemps = sum(exe_homeowner),
    tax_code_rate = first(tax_code_rate), 
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  mutate(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>%
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())

taxcode_taxrates <- taxcodes_current %>% 
  select(tax_code, cur_comp_TC_rate, new_comp_TC_rate, current_exemptions, HO_exemps)


mc_burden <- ptax_pins |>
  left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name, class_1dig) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav),
            group_taxes_current = sum(taxed_eav * (tax_code_rate/100)),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T)
     )  %>%
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) %>%
  group_by(clean_name) |>
  
  mutate(
    muni_taxbase = sum(group_taxbase, na.rm=T),
    muni_levy = sum(group_taxes_current, na.rm = T)
  ) %>%
  ungroup() %>%
  mutate(
         pct_eav = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy) |>
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden)*100)


burden_c2 <- mc_burden %>% 
  filter(class_1dig == 2) %>% 
  select(clean_name, pct_eav, burden_shift,  pct_taxburden_current, hyp_pct_taxburden) %>% 
  arrange(pct_eav)

mc_burden %>% 
  filter(clean_name == "Dolton") %>% 
  select(class_1dig, pct_taxburden_current, hyp_pct_taxburden) %>% 
  arrange(desc(pct_taxburden_current))

Table 4.4

Figure 9. Change in Share of Tax Burden

Table 2. Change in share of property tax burden

Table 2: Change in the share of property tax burden due to exemptions for single-family, multi-family, and commercial and industrial properties, tax year 2023

burden_shift <- ptax_pins %>%
    mutate(Group = case_when(
    class_1dig == 2 ~ "Single-family",
    class_1dig %in% c(3, 9) ~ "Multi-family",
     TRUE ~ "Commercial & Industrial"
  )) %>%
    left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name, Group) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav, na.rm = T),
            group_taxes_current = sum(taxed_eav * (tax_code_rate/100), na.rm = T),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T)
     )  %>%
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) %>%
  ungroup() |>
  group_by(clean_name) |>
  mutate(
    muni_taxbase = sum(group_taxbase, na.rm=T),
    muni_levy = sum(group_taxes_current, na.rm = T)
  ) %>%  
  ungroup() %>%
  mutate(
         pct_eav_current = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy) |> 
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden)*100) 

burden_shift <- ptax_pins %>%
  filter(class_1dig != 0) |>
    mutate(Group = case_when(
    class_1dig == 2 ~ "Single-family",
    class_1dig == 3 | class_1dig == 9 ~ "Multi-family",
     TRUE ~ "Commercial & Industrial"
  )) %>%
    left_join(taxcode_taxrates, by = "tax_code") |>
  group_by(clean_name) %>% 
  mutate(muni_levy = sum(final_tax_to_dist, na.rm=TRUE)) %>%
  ungroup() %>%
  group_by(clean_name, Group, muni_levy) |>
  
  ## calculate taxbase from each major class 
  ## and the amount of taxes currently collected from each
  summarize(group_taxbase= sum(taxed_eav, na.rm = T),
            group_taxes_current = sum(taxed_eav * (cur_comp_TC_rate/100), na.rm = T),
            hyp_group_taxbase = sum(taxed_eav + all_exemptions, na.rm = T),
            hyp_group_taxes = sum( (taxed_eav + all_exemptions) * (new_comp_TC_rate/100), na.rm = T))  %>%
  mutate(across(c(group_taxbase:hyp_group_taxes), round, 0)) %>%
  ungroup() |>
 # group_by(clean_name) |>
 # mutate(
   # muni_taxbase = sum(group_taxbase, na.rm=T),
   # muni_levy = sum(group_taxes_current, na.rm = T)
 # ) %>%  
 # ungroup() %>%
  mutate(
     #    pct_eav_current = group_taxbase / muni_taxbase,
         pct_taxburden_current = group_taxes_current / muni_levy,
         hyp_pct_taxburden = hyp_group_taxes / muni_levy)  |> 
  mutate(
         burden_shift = (pct_taxburden_current - hyp_pct_taxburden))

## Current tax burden 
burden_shift %>%  
    select(clean_name, Group, pct_taxburden_current) %>%
pivot_wider(id_cols = clean_name, names_from = Group, values_from = pct_taxburden_current) %>%
  arrange((`Single-family`))

Table 4.5: Current tax burden (levy paid by group / total levy billed by municipality)

Something is wrong with burden shift calculation

burden_shift %>%  
    select(clean_name, Group, burden_shift) %>%
pivot_wider(id_cols=clean_name, names_from = Group, values_from = burden_shift) %>%
  arrange((`Single-family`))

Table 4.6: Change in tax burden, measured in percentage point change.

datatable(burden_c2, rownames = FALSE,
          colnames = c('Municipality' = 'clean_name', 
                       'Burden Shift, Pct Pt Change' = 'burden_shift', 
                       "C2 EAV/Muni EAV" = 'pct_eav', 
                       'Current Tax Burden\nC2 Tax Collected / Muni Levy' = 'pct_taxburden_current', 
                       "Hypothetical Tax Burden\nHyp. C2 Tax Collected / Muni Levy" = 'hyp_pct_taxburden'),
          caption = "Table 2: Current Share of Taxable EAV and Share of Levy Paid by Class 2 Properties"
           ) %>%
  formatPercentage(c(2,3,4,5), digits = 2) %>%
  formatRound(c(3), digits = 2)

Figure 9 Follow up. Current Tax Burden Map

burden_c2 %>%
  left_join(nicknames) %>%
  mutate(agency_name = ifelse(agency_name == "TOWN CICERO", "CITY OF CICERO", agency_name) ) %>%
  full_join(muni_shp, by = c("agency_name" = "AGENCY_DESC")) %>%

  ggplot(aes(fill = pct_taxburden_current)) + 
  geom_sf(aes(geometry = geometry), color = "black") + 
  theme_void() + 
  theme(axis.ticks = element_blank(), axis.text = element_blank())+
  # scale_fill_gradientn(
   scale_fill_stepsn(colors = c("#ffffcc","#a1dab4" ,"#41b6c4","#2c7fb8", "#253494"),
                        show.limits=TRUE, 
                     limits = c(0,1),
                     na.value = "gray70",
                     n.breaks = 6,
                        name = "Burden with \nExemptions", 
                     labels = scales::percent
                     
                    )+
  labs(title = "Current share of property tax burden", 
       subtitle = "for Class = 2 Property Types")

Table 3.

Difference in taxbills for those that do and do not claim homeowner exemption

ptax_pins <- ptax_pins %>% 
  left_join(taxcode_taxrates, by = "tax_code") |>
  mutate(
    bill_current = (final_tax_to_dist + final_tax_to_tif),
    bill_noGHE = new_comp_TC_rate/100*(equalized_av-all_exemptions+exe_homeowner),
    bill_noexemps = new_comp_TC_rate/100*(equalized_av),
    bill_change = bill_noGHE - bill_current)

C2_munistats <- ptax_pins %>% 
  filter(class > 199 & class < 300) %>%
  group_by(clean_name)  %>%
  arrange(av) %>%
  summarize(
    median_eav = round(median(eav)), 
    median_av = round(median(av)), 
    avg_av = round(mean(av)),
    avg_eav = round(mean(eav)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  )

## removes properties that have more than one exemption type
C2_munistats_filtered <- ptax_pins %>% 
  filter(class > 199 & class < 300) %>%
    filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) %>%
  group_by(clean_name)  %>%
  arrange(av) %>%
  summarize(
    median_eav = round(median(eav, na.rm=TRUE)), 
    median_av = round(median(av, na.rm=TRUE)), 
    avg_av = round(mean(av, na.rm=TRUE)),
    avg_eav = round(mean(eav, na.rm=TRUE)),
    C2_pins_in_muni = n(),
    C2_current_exemptions = sum(all_exemptions, na.rm = TRUE),
    C2_HO_exemps = sum(exe_homeowner, na.rm = TRUE),
  )

C2_munistats_filtered 

C2_munistats

## Grouped by if they have a $0 tax bill and had the GHE per muni
## Recalculating for Josh & Rachael
muni_median_summarytable <- ptax_pins %>% 

  filter(class >199 & class < 300) %>% 
  
  # merge in muni residential median AV
  left_join(C2_munistats_filtered) %>%
  
  # +/- 500 from municpalities median residential AV
  filter(av < median_av+200 & av > median_av-200) %>%
  
  # Removes properties that received other types of exemptions
  filter(exe_senior == 0 & 
           exe_freeze == 0 & 
           exe_longtime_homeowner == 0 & 
           exe_disabled == 0 & 
           exe_vet_returning == 0 & 
           exe_vet_dis_lt50 == 0 & 
           exe_vet_dis_50_69 == 0 & 
           exe_vet_dis_ge70 == 0 & 
           exe_abate == 0) %>%
  
  arrange(av) %>%
  
  mutate(bill_current = (final_tax_to_dist + final_tax_to_tif),
         bill_noexemps =  new_comp_TC_rate/100*(equalized_av-all_exemptions+exe_homeowner),
         bill_change = bill_noexemps - bill_current) %>%
  
  group_by(clean_name, # zero_bill, 
           has_HO_exemp) %>% 
  
  summarize(AV = median(median_av), # median_av was calculated earlier: C2 median AV for the muni 
            `Taxable EAV` = round(median(eav)),
            bill_cur = round(median(bill_current)),
            bill_new = round(median(bill_noexemps)),
            bill_change = round(median(bill_change)),
            pincount=n(),
            perceived_savings = round(median(tax_amt_exe))) %>% 
  
  # merge in clean_names variable
  left_join(nicknames) %>%
  
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, everything()) %>%
  select(-c(agency_number, agency_name))

muni_median_summarytable

muni_taxrates <- ptax_pins %>% 
  group_by(clean_name)  %>%
  arrange(av) %>%
  
  summarize(
    muni_median_av = round(median(av, na.rm=TRUE)),
    muni_median_eav = round(median(eav, na.rm=TRUE)),
    muni_mean_av = round(mean(av, na.rm=TRUE)),
    muni_mean_eav = round(mean(eav, na.rm=TRUE)),
    av = sum(av, na.rm = TRUE),
    eav = sum(eav, na.rm = TRUE),
    equalized_AV = sum(equalized_av, na.rm = TRUE),
    pins_in_muni = n(),
    current_exemptions = sum(all_exemptions, na.rm = TRUE),
    HO_exemps = sum(exe_homeowner, na.rm = TRUE),
    tax_code_rate = mean(tax_code_rate, na.rm = TRUE), # Changed from first() to mean() on Nov 1
    final_tax_to_dist = sum(final_tax_to_dist, na.rm = TRUE), # used as LEVY amount!! 
    final_tax_to_tif = sum(final_tax_to_tif, na.rm = TRUE),
    tax_amt_exe = sum(tax_amt_exe, na.rm = TRUE), 
    tax_amt_pre_exe = sum(tax_amt_pre_exe, na.rm = TRUE), 
    tax_amt_post_exe = sum(tax_amt_post_exe, na.rm = TRUE),
    rpm_tif_to_cps = sum(rpm_tif_to_cps, na.rm = TRUE), # not used
    rpm_tif_to_rpm = sum(rpm_tif_to_rpm, na.rm=TRUE), # not used
    rpm_tif_to_dist = sum(rpm_tif_to_dist, na.rm=TRUE), # not used
    tif_share = mean(tif_share, na.rm=TRUE), # not used
  ) %>%
  
  mutate(total_bill_current = final_tax_to_dist + final_tax_to_tif) %>%
  rename(cur_comp_TC_rate = tax_code_rate) %>%
  mutate(current_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100),
         new_taxable_eav = final_tax_to_dist/(cur_comp_TC_rate/100) + HO_exemps) %>%
  mutate(new_comp_TC_rate = (final_tax_to_dist / new_taxable_eav)*100) %>%
  
  mutate(new_comp_TC_rate = ifelse(is.nan(new_comp_TC_rate), cur_comp_TC_rate, new_comp_TC_rate)) %>% 
  
  left_join(C2_munistats) %>%
  left_join(nicknames) %>%
  mutate(rate_change = cur_comp_TC_rate - new_comp_TC_rate,
    nobillchange_propertyEAV = round(10000 * ((cur_comp_TC_rate/100) / (rate_change/100))),
    nochange_av = round(nobillchange_propertyEAV / eq_factor),
    nochange_ratio = nochange_av / median_av) %>%
  select(clean_name, C2median_av = median_av, muni_median_av,  C2mean_av = avg_av, muni_mean_av, cur_comp_TC_rate, new_comp_TC_rate, current_taxable_eav, new_taxable_eav, everything())

muni_taxrates 

muni_taxrates %>% left_join(nicknames) %>% select(clean_name, muni_median_av, C2median_av, nochange_av )

muni_median_summarytable %>% 
  left_join(muni_taxrates) %>% 
  ungroup() %>%
  select(clean_name, has_HO_exemp, bill_cur, bill_new, bill_change, perceived_savings,  AV, `Taxable EAV`, nochange_av, nochange_ratio) #%>%

ptax_pins |> 
  filter(clean_name %in% c("Chicago", "Dolton", "Glencoe")) |>
  filter(between(av, 14500, 15000)) %>%
  filter(class_1dig == 2) |> 
  filter(exe_senior == 0 & exe_freeze == 0 ) |>
  group_by(clean_name, has_HO_exemp) %>%
  arrange(av) %>%
  
  summarize(
            median_bill = median(total_billed, na.rm=TRUE),
            median_AV = median(av, na.rm= TRUE),
            eav = median(eav, na.rm=TRUE),
            # class = median(class),
            type = "Current Exemptions",
            N = n())

Table 4.

Municipalities with the largest and smallest reductions in tax base (as a share of residential EAV due to exemptions) and median property values

Figure 10. Tax Burden Shift from Current GHE

Share of municipal property tax levy paid by Class 2 properties with and without homestead exemptions, tax year 2021

# as a dot graph ## 

order <- mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift)

slice <-  mc_burden |>
  filter(class_1dig == 2) |>
    select(clean_name, pct_taxburden_current, burden_shift) %>%
  arrange(pct_taxburden_current) %>%
  slice(1:5, 63:67, 127:131)



median_burden <- median(order$pct_taxburden_current)
median_shift <- median(order$burden_shift)

# median burden change is 5.9 percentage points
# current median burden is 70.3% of the levy

mc_burden %>% 
  filter(clean_name %in% slice$clean_name) %>%
#filter(!clean_name %in% cross_county_lines$clean_name)%>%
  filter(class_1dig == 2) |>
#  filter(burden_current > 0.938 |burden_current < .17 |
 #          ( (burden_current < median(burden_current) + 0.01 )& (burden_current > median(burden_current) - 0.01)) )%>% 
  ungroup() %>% 
      select(clean_name, pct_taxburden_current, hyp_pct_taxburden, burden_shift) %>%
  arrange(burden_shift) %>%
 # mutate(  burden_noexemps = ifelse(burden_noexemps > 1, 1, burden_noexemps)) %>%
  pivot_longer(c("pct_taxburden_current", "hyp_pct_taxburden"), 
               names_to = "type", values_to = "pct_burden") %>% 
  inner_join(order) %>%
  ggplot(aes(x = pct_burden*100, 
             y= reorder(clean_name, -pct_taxburden_current)))+
  # y= reorder(clean_name, burden_current)))+
  geom_vline(xintercept = 70.2, linetype = 3)+
  geom_line(aes(group = clean_name))+ 
  geom_hline(yintercept = 5.5, linetype = 2)+
  geom_hline(yintercept = 10.5, linetype = 2)+
  geom_point(aes(color=type), size=3 )+

  theme_minimal() + 
  theme(#legend.position = "none", 
    legend.title = element_blank(),
    plot.title.position = "plot",
    #   panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA) #transparent plot bg
  )+
  scale_color_brewer(palette="Paired", labels = c("Current Burden", "Burden if \nNo Exemptions" ), direction = 1)+

  
  labs(title = "Change in Class 2 Residential Tax Burden", 
       subtitle = "Ordered by Current Tax Burden",
  x = "Share of Levy (%)", y = "" , 
  caption = "Dotted line represents median Class 2 burden (65.5% of the levy). Residential Tax Burden is the 
  share of the property tax collected that was paid for by property owners with Class 2 properties.") +
    geom_label(label = "Class 2 pays small share of \nlevy; very little residential", x=32, y = 13, label.size = 1, size = 3)+
    geom_label(label = "Class 2 pays median share of \nlevy (70.3%), mix of land use", x=42, y = 7.5, label.size = 1, size = 3) +
    geom_label(label = "Class 2 pays nearly all of levy, \nhighly residential", x=70, y = 3, label.size = 1,size = 3)

Figure 4.1

Figure 11. Zero Dollar Bills

muni_mc_sums %>%
  filter(major_class_code == 2) %>% 
  group_by(year) %>%
  summarize(zerodollar_count = sum(zero_bill)) %>% 
  ggplot(aes(x=year, y = zerodollar_count)) + 
  geom_bar(position = "stack", stat = "identity") + theme_minimal()

smallbills <- ptax_pins |> 
  filter(class_1dig == 2 & eav < 150 ) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, 
         final_tax_to_dist, pin, everything()) |>
  arrange(total_billed) %>% arrange(desc(total_billed)) %>%
  filter(total_billed > 0)

smallbills

smallbills |>
  summarize(n = n(), 
            exe_freeze = sum(exe_freeze, na.rm=T),
            exe_senior = sum(exe_senior, na.rm=T),
            exe_homeowner = sum(exe_homeowner, na.rm=T))

smallbills |>
  group_by(clean_name) %>%
  summarize(n = n(), 
            exe_freeze = sum(exe_freeze, na.rm=T),
            exe_senior = sum(exe_senior, na.rm=T),
            exe_homeowner = sum(exe_homeowner, na.rm=T))

smallbills |> 
  select(clean_name, class, eav, equalized_av, av, fmv, 
         total_billed, final_tax_to_dist, pin)

zerobills <- ptax_pins |> 
  filter(class_1dig == 2 & eav > 0 & total_billed == 0) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, final_tax_to_dist, pin, exe_homeowner, exe_senior, exe_freeze, everything())  %>%
  arrange(desc(exe_freeze))

zerobills

options(scipen = 999)

zerobills %>% 
  reframe(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE), 
          .by = clean_name) |> 
  arrange(desc(exe_freeze))

zerobills %>% 
  summarize(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE)) |> 
  arrange(desc(exe_freeze))

zerobills %>% 
  group_by(Triad) %>%
  summarize(n = n(), total_eav = sum(taxed_eav + exempt_eav, na.rm=TRUE),
          exe_homeowner = sum(exe_homeowner), exe_senior = sum(exe_senior), 
          exe_freeze = sum(exe_freeze), taxed_eav = sum(taxed_eav, na.rm=TRUE)) |> 
  arrange(desc(exe_freeze))

ptax_pins |> 
  filter(class_1dig == 2 & eav > 0 & total_billed == 0) |>
  select(clean_name, class, eav, equalized_av, av, fmv, total_billed, final_tax_to_dist, pin, exe_homeowner, exe_senior, exe_freeze)  %>%
  arrange(desc(exe_freeze)) %>%
  summarize(max = max(eav),
            min = min(eav),
            median = median(eav),
            n = n())